Trustworthy Artificial Intelligence and Process Mining: Challenges and Opportunities

TL;DR

This paper explores how process mining can enhance the transparency, monitoring, and compliance of AI governance practices, addressing challenges in ensuring trustworthy AI across organizational and regulatory landscapes.

Contribution

It demonstrates the application of process mining techniques to improve visibility, detect bottlenecks, and automate compliance monitoring in Trustworthy AI processes.

Findings

Process mining provides fact-based visibility into AI compliance processes.

It helps identify bottlenecks and uncertainties in AI governance workflows.

Automated analysis can support remediation and continuous monitoring.

Abstract

The premise of this paper is that compliance with Trustworthy AI governance best practices and regulatory frameworks is an inherently fragmented process spanning across diverse organizational units, external stakeholders, and systems of record, resulting in process uncertainties and in compliance gaps that may expose organizations to reputational and regulatory risks. Moreover, there are complexities associated with meeting the specific dimensions of Trustworthy AI best practices such as data governance, conformance testing, quality assurance of AI model behaviors, transparency, accountability, and confidentiality requirements. These processes involve multiple steps, hand-offs, re-works, and human-in-the-loop oversight. In this paper, we demonstrate that process mining can provide a useful framework for gaining fact-based visibility to AI compliance process execution, surfacing compliance bottlenecks, and providing for an automated approach to analyze, remediate and monitor uncertainty in AI regulatory compliance processes.